AI-Driven Reliable SFC Deployment: Challenges, Opportunities, and Perspectives

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Abstract
Artificial Intelligence (AI) techniques are driving increasing volumes of reliability-sensitive Service Function Chain (SFC) requests from applications such as autonomous driving, smart manufacturing, and intelligent healthcare. These applications require not only feasible deployment but also continuous service availability under failures, disruptions, and uncertainty. Existing reliable SFC deployment studies have largely relied on rule-based approaches, which provide explicit formulations and interpretable decision rules but often struggle with dynamic, large-scale, and multi-objective requirements involving reliability, latency, cost, and security. AI-driven solutions therefore offer a promising direction for enabling adaptive and data-driven deployment decisions. This survey first clarifies the workflow of SFC deployment and the distinction between conventional and reliable SFC deployment. We then review rule-based reliable SFC deployment, analyzing its major design rationales, representative solution paradigms, and limitations. Building on this analysis, we examine AI-driven methodologies for reliable SFC deployment, with a particular focus on deep reinforcement learning approaches, which dominate the existing literature. Finally, we discuss open challenges and future directions toward adaptive, robust, and scalable reliable SFC deployment.

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